Accurate and fast multiple-testing correction in eQTL studies.
Am J Hum Genet
; 96(6): 857-68, 2015 Jun 04.
Article
em En
| MEDLINE
| ID: mdl-26027500
ABSTRACT
In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Variação Genética
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Regulação da Expressão Gênica
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Interpretação Estatística de Dados
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Locos de Características Quantitativas
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Genes
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article